We believe that the effective combination of these RL paradigms will enable the learning of complex skills on real robots [1]}, [2]}. In this paper, we address this challenge by investigating optimal methods of guiding policy learning, while also learning the model dynamics. Intuitively, having interweaving MB-MF components can potentially allow scheduling of separate solutions according to different phases of learning, i.e. MB when action planning is required, and MF when additional exploration is needed.
